Artificial Intelligence Engineer

HTC Global Services; Bengaluru; On-site; INR 2,500,000 - 6,500,000; Full time

Job summary

HTC Global Services in Bengaluru seeks experienced AI engineers to design, provision, and maintain scalable AI infrastructure on AWS for AI/ML applications across development, testing, and production environments. Responsibilities include deploying and troubleshooting AI models, implementing observability, and managing CI/CD pipelines with Terraform/Scalr, Docker, and Kubernetes.

You will collaborate with DevOps and security teams to ensure SLA adherence and cost efficiency.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field.
• Hands-on Python development for AI/ML, data engineering, or cloud-based apps.
• Experience with ML/LLM workflows, model training, evaluation, and deployment.
• Experience with AWS cloud services for scalable AI/ML apps.
• Knowledge of API, microservices, and containerized deployments in enterprise environments.

Responsibilities

• Design, provision, configure, and maintain scalable AI infrastructure on AWS for AI/ML apps.
• Deploy, manage, and troubleshoot AI/ML models, endpoints, and APIs in production.
• Implement monitoring, logging, alerting, and observability for AI apps.
• Provide L2/L3 production support, investigate incidents, and perform root cause analysis.
• Manage infrastructure provisioning with Terraform/Scalr for secure deployments.
• Troubleshoot infrastructure, networking, API integration, and containerization issues.
• Implement and maintain CI/CD pipelines for automated AI deployment and testing.
• Monitor AI workloads for latency, throughput, resource use, and costs.
• Support Docker/Kubernetes-based apps with EKS/ECS for scalability and reliability.
• Maintain runbooks, incident reports, and disaster recovery with cross-team collaboration.

Skills

Python

AI/ML Infra

AWS

Terraform

Kubernetes

LLMs

CI/CD

Monitoring

DevOps

Education

Bachelor's degree in Computer Science / Engineering or related field

Tools

Docker

Terraform

Kubernetes

EKS/ECS

Scalr

Job description

We are seeking a highly skilled AI engineers with Infra and support experience..

Responsibilities include but are not limited to:

Design, provision, configure, and maintain scalable AI infrastructure on AWS to support AI/ML applications across development, testing, and production environments.

• Deploy, manage, and troubleshoot AI/ML models, LLM applications, inference endpoints, and APIs in production environments.
• Implement monitoring, logging, alerting, and observability for AI applications using AWS CloudWatch and other monitoring tools to ensure system availability and performance.
• Provide L2/L3 production support for AI applications, investigate incidents, perform root cause analysis, and implement corrective and preventive actions.
• Manage cloud infrastructure provisioning and configuration using Terraform/Scalr, ensuring secure, consistent, and repeatable deployments.
• Troubleshoot infrastructure, application, networking, API integration, containerization, and model inference issues to minimize service disruptions.
• Implement and maintain CI/CD pipelines for automated AI application deployment, testing, version control, release management, and rollback procedures.
• Monitor AI workloads for latency, throughput, resource utilization, error rates, and operational costs, implementing performance and cost optimization measures.
• Support containerized AI applications using Docker and Kubernetes, including Amazon EKS/ECS, with a focus on scalability, availability, and reliability.
• Maintain operational runbooks, incident reports, support documentation, and disaster recovery procedures while collaborating with DevOps, platform, security, and application support teams to meet SLAs.

Required Skills

• Bachelor’s degree in computer science, Engineering, Data Science, Artificial Intelligence, or a related technical field.
• hands‑on experience in Python and software development for AI, ML, data engineering, or cloud‑based applications.
• experience in Machine Learning / Data Science, including feature engineering, model training, experimentation, evaluation, and deployment.
• experience in LLMs / Generative AI use cases such as prompt engineering, prompt optimization, RAG, embeddings, vector databases, evaluation frameworks, and guardrails.
• Strong experience with Prompt Engineering, including prompt design patterns, prompt chaining, instruction design, context‑window optimization, role/task prompting, structured response generation, and prompt tuning for enterprise use cases.
• Strong understanding of Agentic AI patterns, including multi‑step reasoning workflows, tool usage, memory/context handling, orchestration frameworks, and autonomous task execution.
• Strong experience with AWS cloud services for developing, deploying, and managing scalable AI/ML and data‑driven applications.
• Hands‑on experience with Terraform and/or Scalr for infrastructure as code and environment provisioning.
• Experience with APIs, microservices, and containerized deployments in enterprise environments.
• Experience with SQL and NoSQL databases, vector databases, and data integration across structured and unstructured sources.
• Strong knowledge of software engineering fundamentals including version control, testing, CI/CD, secure coding, code reviews, and release management.
• Experience presenting technical solutions, results, and recommendations to business stakeholders and leadership.
• Strong analytical thinking, problem‑solving, communication, and cross‑functional collaboration skills.

Preferred Skills

• Experience in financial services, investment management, compliance, risk, or other regulated enterprise environments.
• Experience with MLOps / LLMOps tools and practices for deployment, monitoring, evaluation, and lifecycle management of AI systems.
• Familiarity with frameworks and tools such as LangChain, LangGraph, LlamaIndex, Hugging Face, OpenAI APIs, Amazon Bedrock, MLflow, Airflow, Spark, or similar technologies.
• Experience designing and optimizing prompts for RAG workflows, agentic systems, tool calling, reasoning tasks, workflow automation, and domain‑specific enterprise AI applications.
• Experience with semantic search, vector retrieval, document intelligence, conversational AI, and knowledge‑grounded AI systems.
• Knowledge of Responsible AI, model governance, explainability, bias detection, risk controls, and enterprise AI safety practices.
• Experience working in Agile / Scrum delivery models.
• Experience leading or mentoring teams and driving implementation across multiple stakeholders.
• Advanced expertise in AI/LLMs with strong hands‑on experience in end‑to‑end model development and deployment.
• Solid data engineering background with experience in building and managing complex, large‑scale data systems.
• Proven ability to work effectively in large, collaborative teams within fast‑paced, enterprise environments.
• Strong ownership and accountability for end‑to‑end delivery
• Ability to translate ambiguous business problems into practical AI solutions
• Strong collaboration across engineering, product, business, and governance teams
• Technical leadership in solution design and implementation
• Strong communication and stakeholder management skills
• Continuous learning mindset with focus on emerging AI technologies and best practices